OpenRaiser/Intern-Atlas huggingface dataset:
State Estimation in Electric Power Systems Using Belief Propagation: An Extended DC Model
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we model an extended DC state estimation (SE) in an electric power system as a factor graph (FG) and solve it using belief propagation (BP) algorithm. The DC model comprises bus voltage angles as state variables, while the extended DC model includes bus voltage angles and bus voltage magnitudes as state variables. By applying BP to solve the SE problem in the extended DC model, we obtain a Gaussian BP scenario for which we derive closed-form expressions for BP messages exchanged along the FG. The performance of the BP algorithm is demonstrated for the IEEE 14 bus test case. Finally, the application of BP algorithm on the extended DC scenario provides significant insights into a fundamental structure of BP equations in more complex models such as the AC model - the topic we will investigate in our follow up work. As a side-goal of this paper, we aim at thorough and detailed presentation on applying BP on the SE problem in order to make the powerful BP algorithm more accessible and applicable within the power-engineering community.
Benign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features
Venue: arXiv cs.CV 2016
Year: 2016

Lung cancer accounts for the highest number of cancer deaths globally. Early diagnosis of lung nodules is very important to reduce the mortality rate of patients by improving the diagnosis and treatment of lung cancer. This work proposes an automated system to classify lung nodules as malignant and benign in CT images. It presents extensive experimental results using a combination of geometric and histogram lung nodule image features and different linear and non-linear discriminant classifiers. The proposed approach is experimentally validated on the LIDC-IDRI public lung cancer screening thoracic computed tomography (CT) dataset containing nodule level diagnostic data. The obtained results are very encouraging correctly classifying 82% of malignant and 93% of benign nodules on unseen test data at best.
Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems
Venue: arXiv cs.AI 2016
Year: 2016

Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of Automated Fare Collection(AFC) systems opens up a new opportunity. However, only each trip's tap-in and tap-out timestamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, which are necessary to solve our problem. While existing methods work well in some specific situations, they don't work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large scale data set collected from the Shenzhen metro system. The measured results provide us with useful inputs when building the passenger path choice model.
Robust Downbeat Tracking Using an Ensemble of Convolutional Networks
Venue: arXiv cs.SD 2016
Year: 2016

In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm and bass content to feed convolutional neural networks that are adapted to take advantage of each feature characteristics. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the metrical continuity of a song. We then perform an evaluation of our system on a large base of 9 datasets, compare its performance to 4 other published algorithms and obtain a significant increase of 16.8 percent points compared to the second best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.
CITlab ARGUS for historical handwritten documents
Venue: arXiv cs.CV 2016
Year: 2016

We describe CITlab's recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET's ARGUS framework for intelligent text recognition and image processing.
The State of the Art in Cartograms
Venue: arXiv cs.HC 2016
Year: 2016

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges.
Mixtape Application: Last.fm Data Characterization
Venue: arXiv cs.SI 2016
Year: 2016

This report analyses data collected from Last.fm and used to create a real-time recommendation system. We collected over 2M songs and 1M tags and 372K user's listening habits. We characterize users' profiles: age, playcount, friends, gender and country. We characterized song, artist and tag popularity, genres of songs. Additionally we evaluated the co-occurrence of songs in users' histories, which can be used to compute similarity between songs.
Mixtape Application: Music Map Methodology and Evaluation
Venue: arXiv cs.SI 2016
Year: 2016

This report discusses dimensionality reduction techniques used to create a music map - a map where the distances between songs represent their similarity and that can be used to recommend songs. We evaluate two techniques: Isomap and L-Isomap.
Journeys & Notes: Designing Social Computing for Non-Places
Venue: arXiv cs.HC 2016
Year: 2016

In this work we present a mobile application we designed and engineered to enable people to log their travels near and far, leave notes behind, and build a community around spaces in between destinations. Our design explores new ground for location-based social computing systems, identifying opportunities where these systems can foster the growth of on-line communities rooted at non-places. In our work we develop, explore, and evaluate several innovative features designed around four usage scenarios: daily commuting, long-distance traveling, quantified traveling, and journaling. We present the results of two small-scale user studies, and one large-scale, world-wide deployment, synthesizing the results as potential opportunities and lessons learned in designing social computing for non-places.
Degrees of Freedom of $M\times N$ SISO X Channel with Synergistic Alternating CSIT
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, degrees of freedom (DoF) is investigated for the $M\times N$ single input single output (SISO) X channel with alternating channel state information at the transmitters (CSIT). It is known that the sum DoF of 2-user SISO X channel with synergistic alternating CSIT is the same as the sum DoF of 2-user $(M=N=2)$ SISO X channel with perfect CSIT [8]. In this paper, such 2-user X channel schemes are extended to the general $M\times N$ X channel. It is shown that the proposed $M\times N$ X channel schemes with synergistic alternating CSIT achieve $2M/(M+1)$ sum DoF. This DoF with $M=N=K$ is strictly lager than the best known DoF for the $K$-user X channel with delayed CSIT.
An Overview of End-to-End Verifiable Voting Systems
Venue: arXiv cs.CR 2016
Year: 2016

Advances in E2E verifiable voting have the potential to fundamentally restore trust in elections and democratic processes in society. In this chapter, we provide a comprehensive introduction to the field. We trace the evolution of privacy and verifiability properties in the research literature and describe the operations of current state-of-the-art E2E voting systems. We also discuss outstanding challenges to the deployment of E2E voting systems, including technical, legal, and usability constraints. Our intention, in writing this chapter, has been to make the innovations in this domain accessible to a wider audience. We have therefore eschewed description of complex cryptographic mechanisms and instead attempt to communicate the fundamental intuition behind the design of E2E voting systems. We hope our work serves as a useful resource and assists in the future development of E2E voting.
CrowdSource: Automated Inference of High Level Malware Functionality from Low-Level Symbols Using a Crowd Trained Machine Learning Model
Venue: arXiv cs.CR 2016
Year: 2016

In this paper we introduce CrowdSource, a statistical natural language processing system designed to make rapid inferences about malware functionality based on printable character strings extracted from malware binaries. CrowdSource "learns" a mapping between low-level language and high-level software functionality by leveraging millions of web technical documents from StackExchange, a popular network of technical question and answer sites, using this mapping to infer malware capabilities. This paper describes our approach and provides an evaluation of its accuracy and performance, demonstrating that it can detect at least 14 high-level malware capabilities in unpacked malware binaries with an average per-capability f-score of 0.86 and at a rate of tens of thousands of binaries per day on commodity hardware.
Near-Optimal Fingerprinting with Constraints
Venue: arXiv cs.CR 2016
Year: 2016

Several recent studies have demonstrated that people show large behavioural uniqueness. This has serious privacy implications as most individuals become increasingly re-identifiable in large datasets or can be tracked while they are browsing the web using only a couple of their attributes, called as their fingerprints. Often, the success of these attacks depend on explicit constraints on the number of attributes learnable about individuals, i.e., the size of their fingerprints. These constraints can be budget as well as technical constraints imposed by the data holder. For instance, Apple restricts the number of applications that can be called by another application on iOS in order to mitigate the potential privacy threats of leaking the list of installed applications on a device. In this work, we address the problem of identifying the attributes (e.g., smartphone applications) that can serve as a fingerprint of users given constraints on the size of the fingerprint. We give the best fingerprinting algorithms in general, and evaluate their effectiveness on several real-world datasets. Our results show that current privacy guards limiting the number of attributes that can be queried about individuals is insufficient to mitigate their potential privacy risks in many practical cases.
Stacking With Auxiliary Features
Venue: arXiv cs.CL 2016
Year: 2016

Ensembling methods are well known for improving prediction accuracy. However, they are limited in the sense that they cannot discriminate among component models effectively. In this paper, we propose stacking with auxiliary features that learns to fuse relevant information from multiple systems to improve performance. Auxiliary features enable the stacker to rely on systems that not just agree on an output but also the provenance of the output. We demonstrate our approach on three very different and difficult problems -- the Cold Start Slot Filling, the Tri-lingual Entity Discovery and Linking and the ImageNet object detection tasks. We obtain new state-of-the-art results on the first two tasks and substantial improvements on the detection task, thus verifying the power and generality of our approach.
On the Capacity Region of ALOHA Protocol for the Internet of Things
Venue: arXiv cs.NI 2016
Year: 2016

Accommodating the needs of a large number of diverse users in the Internet of Things (IoT), notably managing how the users access the common channel, has posed unique challenges to the network designers. In this paper, we study a heterogeneous IoT network consisting of multiple classes of users who may have different service requirements. For this network, we consider the application of irregular repetition slotted ALOHA (IRSA) that is shown to offer large throughput for single-class networks. Then, we focus on finding the network performance boundaries by studying the set of feasible throughput values for each class, called the capacity region. To this end, we first introduce the concept of dual network of a multi-class network meaning a homogeneous network with the same number of users. We then prove that finding the capacity region of the assumed multi-class network boils down to finding the maximum achievable throughput of its dual network. Using this finding, we then discuss how any given point of the capacity region can be achieved. Further, a delay performance study is conducted to evaluate the average and maximum packet transmission delay experienced by the users of each class.
Analysing the Effects of Routing Centralization on BGP Convergence Time
Venue: arXiv cs.NI 2016
Year: 2016

Software-defined networking (SDN) has improved the routing functionality in networks like data centers or WANs. Recently, several studies proposed to apply the SDN principles in the Internet's inter-domain routing as well. This could offer new routing opportunities and improve the performance of BGP, which can take minutes to converge to routing changes. Previous works have demonstrated that centralization can benefit the functionality of BGP, and improve its slow convergence that causes severe packet losses and performance degradation. However, due to (a) the fact that previous works mainly focus on system design aspects, and (b) the lack of real deployments, it is not clearly understood yet to what extent inter-domain SDN can improve performance. To this end, in this work, we make the first effort towards analytically studying the effects of routing centralization on the performance of inter-domain routing, and, in particular, the convergence time of BGP. Specifically, we propose a Markovian model for inter-domain networks, where a subset of nodes (domains) coordinate to centralize their inter-domain routing. We then derive analytic results that quantify the BGP convergence time under various network settings (like, SDN penetration, topology, BGP configuration, etc.). Our analysis and results facilitate the performance evaluation of inter-domain SDN networks, which have been studied (till now) only through simulations/emulations that are known to suffer from high time/resource requirements and limited scalability.
Online Bayesian Collaborative Topic Regression
Venue: arXiv cs.LG 2016
Year: 2016

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (e.g., LDA) for recommender systems, which has gained increasing successes in many applications. Despite enjoying many advantages, the existing CTR algorithms have some critical limitations. First of all, they are often designed to work in a batch learning manner, making them unsuitable to deal with streaming data or big data in real-world recommender systems. Second, the document-specific topic proportions of LDA are fed to the downstream PMF, but not reverse, which is sub-optimal as the rating information is not exploited in discovering the low-dimensional representation of documents and thus can result in a sub-optimal representation for prediction. In this paper, we propose a novel scheme of Online Bayesian Collaborative Topic Regression (OBCTR) which is efficient and scalable for learning from data streams. Particularly, we {\it jointly} optimize the combined objective function of both PMF and LDA in an online learning fashion, in which both PMF and LDA tasks can be reinforced each other during the online learning process. Our encouraging experimental results on real-world data validate the effectiveness of the proposed method.
On the Optimality of Time Division for Broadcast Channels
Venue: arXiv cs.IT 2016
Year: 2016

We provide a necessary and sufficient condition that under some technical assumption characterizes all two-receiver broadcast channels for which time division is optimal for transmission of private messages.
Improving Crowdsourced Live Streaming with Aggregated Edge Networks
Venue: arXiv cs.MM 2016
Year: 2016

Recent years have witnessed a dramatic increase of user-generated video services. In such user-generated video services, crowdsourced live streaming (e.g., Periscope, Twitch) has significantly challenged today's edge network infrastructure: today's edge networks (e.g., 4G, Wi-Fi) have limited uplink capacity support, making high-bitrate live streaming over such links fundamentally impossible. In this paper, we propose to let broadcasters (i.e., users who generate the video) upload crowdsourced video streams using aggregated network resources from multiple edge networks. There are several challenges in the proposal: First, how to design a framework that aggregates bandwidth from multiple edge networks? Second, how to make this framework transparent to today's crowdsourced live streaming services? Third, how to maximize the streaming quality for the whole system? We design a multi-objective and deployable bandwidth aggregation system BASS to address these challenges: (1) We propose an aggregation framework transparent to today's crowdsourced live streaming services, using an edge proxy box and aggregation cloud paradigm; (2) We dynamically allocate geo-distributed cloud aggregation servers to enable MPTCP (i.e., multi-path TCP), according to location and network characteristics of both broadcasters and the original streaming servers; (3) We maximize the overall performance gain for the whole system, by matching streams with the best aggregation paths.
New Signal Designs for Enhanced Spatial Modulation
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we present three new signal designs for Enhanced Spatial Modulation (ESM), which was recently introduced by the present authors. The basic idea of ESM is to convey information bits not only by the index(es) of the active transmit antenna(s) as in conventional Spatial Modulation (SM), but also by the types of the signal constellations used. The original ESM schemes were designed with reference to single-stream SM and involved one or more secondary modulations in addition to the primary modulation. Compared to single-stream SM, they provided either higher throughput or improved signal-to-noise ratio (SNR). In the present paper, we focus on multi-stream SM (MSM) and present three new ESM designs leading to increasing SNR gains when they are operated at the same spectral efficiency. The secondary signal constellations used in the first two designs are based on a single geometric interpolation step in the signal constellation plane, while the third design also makes use of additional constellations derived through a second interpolation step. The new ESM signal designs are described for MIMO systems with four transmit antennas two of which are active, but we also briefly present extensions to higher numbers of antennas. Theoretical analysis and simulation results indicate that the proposed designs provide a significant SNR gain over MSM.
Construction of Non-expandable Non-overlapping Sets of Pictures
Venue: arXiv cs.FL 2016
Year: 2016

The non-overlapping sets of pictures are sets such that no two pictures in the set (properly) overlap. They are the generalization to two dimensions of the cross-bifix-free sets of strings. Non-overlapping sets of pictures are non-expandable when no other picture can be added without violating the property. We present a construction of non-expandable non-overlapping (NENO) sets of pictures and show some examples of application.
Census Tract License Areas: Disincentive for Sharing the 3.5GHz band?
Venue: arXiv cs.CY 2016
Year: 2016

Flexible licensing model is a necessary enabler of the technical and procedural complexities of Spectrum Access System (SAS)-based sharing framework. The purpose of this study is to explore the effectiveness of 3.5GHz Licensing Framework - based on census tracts as area units, areas whose main characteristic is population. As such, the boundary of census tract does not follow the edge of wireless network coverage. We demonstrate why census tracts are not suitable for small cell networks licensing, by (1) gathering and analysing the official census data, (2) exploring the boundaries of census tracts which are in the shape of nonconvex polygons and (3) giving a measure of effectiveness of the licensing scheme through metrics of area loss and the number of people per census tract with access to spectrum. Results show that census tracts severely impact the effectiveness of the licensing framework since almost entire strategically important cities in the U.S. will not avail from spectrum use in 3.5GHz band. Our paper does not seek to challenge the core notion of geographic licensing concept, but seeks a corrective that addresses the way the license is issued for a certain area of operation. The effects that inappropriate size of the license has on spectrum assignments lead to spectrum being simply wasted in geography, time and frequency or not being assigned in a fair manner. The corrective is necessary since the main goal of promoting innovative sharing in 3.5 GHz band is to put spectrum to more efficient use.
Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks
Venue: arXiv cs.NI 2016
Year: 2016

We consider joint caching, routing, and channel assignment for video delivery over coordinated small-cell cellular systems of the future Internet. We formulate the problem of maximizing the throughput of the system as a linear program in which the number of variables is very large. To address channel interference, our formulation incorporates the conflict graph that arises when wireless links interfere with each other due to simultaneous transmission. We utilize the column generation method to solve the problem by breaking it into a restricted master subproblem that involves a select subset of variables and a collection of pricing subproblems that select the new variable to be introduced into the restricted master problem, if that leads to a better objective function value. To control the complexity of the column generation optimization further, due to the exponential number of independent sets that arise from the conflict graph, we introduce an approximation algorithm that computes a solution that is within $ε$ to optimality, at much lower complexity. Our framework demonstrates considerable gains in average transmission rate at which the video data can be delivered to the users, over the state-of-the-art Femtocaching system, of up to 46%. These operational gains in system performance map to analogous gains in video application quality, thereby enhancing the user experience considerably.